One of the important issues in power and performance trade off analysis in Network on Chip designs is communication. Communication portion in the power consumption of System on Chip can be up to 50% of the whole power of consumption of the chip. This deems to be more important for Network on Chips which center around an intercommunication networks. Many Networks on Chip routers have been designed; however most of them have not been implemented until now. In this paper, design and implementation of a synchronous Network on Chip router based on asynchronous communication mechanism are presented. We designed a router with scalability feature which is synthesized in both FPGA and ASIC infrastructures. In addition, the proposed router uses low resource utilization percentage of FPGA and ASIC.

Two new high-performance Full Adders, purely designed with 3-input Majority-not function, are proposed in this paper. The Majority-not function is implemented efficiently by using only capacitors and a static CMOS inverter. This kind of design improves the parameters of the Full Adder cell and leads to high performance, driving capability, a high degree of regularity and simplicity. Five state-of-the-art 1-bit Full Adder cells and the proposed Full Adders are simulated using 0.18μm CMOS technology at three supply voltages. Simulation results demonstrate significant improvement in terms of power consumption and Power-Delay Product (PDP).

The embedded systems usage in different applications is prevalent in recent years. These systems include a wide range of equipments from cell phones to medical instruments, which consist of hardware and software. In many examples of embedded systems, fault occurrence can lead to serious dangers in system behavior (for example in satellites). Therefore, we try to increase the fault tolerance feature in these systems. Therefore, we need some mechanisms that increase the robustness and reliability of such systems. These objects cause the on-line test to be a great concern. It is not important that these mechanisms work in which level (Hardware level, Software level or Firmware). The major concern is that how well these systems can provide debugging, test and verification features for the user regardless of their implementation levels. Background Debug Module is a real time tool for these features. In this paper we apply an innovative way to use the BDM tool for fault injection in an embedded system.

Binary Arithmetic Coding (BAC) is among the techniques used in H.264 video coding standard to improve the coding efficiency. BAC includes an iterative process of renormalization with up to seven iterations for coding each symbol. Since BAC is also a computational intensive unit in H.264 encoder, various hardware realizations have been proposed for it in the literature. In this paper, we propose a hardware implementation for BAC, which uses lookup table to avoid the iterative coding process and achieves coding rate of one symbol per clock at 260 MHz clock rate. Post synthesize simulation results indicate that the proposed architecture is a resource and speed efficient hardware for H.264 binary arithmetic encoder.

Asynchronous protocols exhibit various noise robustness and when used in GALS NoC links, they can directly affect the signal integrity. In this paper we study the noise robustness of two well-known asynchronous protocols, namely Dual-Rail (DRP) and Bundled-Data (BDP) in the GALS NoC links, and subsequently confirm our claims through simulations. We apply an enhanced version of BDP and DRP to 32/64 parallel line links, show results in terms of noise robustness using global interconnect features, specified in the ITRS roadmap for 32nm technology. The simulation results for two thousand random generated inputs show that the number and the amplitude of noise glitches over ‘0’ state lines as well as the required threshold voltage needed for avoiding errors in BDP link are much lower than in DRP's. Therefore, BDP links can present better signal integrity features and have less overhead compared to DRP's, employing only some simple noise reduction techniques and more timing adjustment effort.

On-chip network interconnections or Network-on- Chip (NOC) is viewed as a possible solution to global wiring issues in highly integrated complex systems. In current NoCs and in order to promote system level integrity, there is a growing need to provide different traffic classes, each with a different Quality-of-Service guarantee. In synchronous NOCs guaranteed service is provided by reserving time slots. Asynchronous NOC implementation, on the other hand, eliminates the need for synchronization when crossing clock domains. In asynchronous NOCs there is no notation of time and arbitration. Any delay in arbitration or refusing requests in arbitration results in the accumulation of data in switch buffers. In this paper a novel arbitration scheme for clockless NOCs has been proposed that is able to service a connection without any halt or jitter in streaming. Consequently, links with a burst traffic pattern and guaranteed bandwidth requirement can be implemented without any large buffers. Simulation results indicate that the proposed method is able to reduce switch buffer size, and hence power consumption in any NoC platform that is providing guaranteed bandwidth requirements in applications with burst data characteristics. For instance, in an MPEG-2 decoder mapped to a 3x2 mesh with 8 guaranteed bandwidth channels in each port, the proposed arbitration scheme is able to reduce the buffer size by 25%. The improvement increases to %47 for a JPEG2000 encoder mapped to a 3x3 mesh.

This paper presents an asynchronous and a synchronous NoC router architecture. The asynchronous scheme is implemented by the help of CSP-Verilog language and the synchronous one is designed employing VHDL language. Their designs are similar except the extra links which are in charge of handshaking processes in asynchronous architecture. According to the experimental results the transition counts of buffer, and switch components in synchronous router are almost 82% and 60% of asynchronous one, respectively. On the other hand, the transition counting of routing unit in asynchronous NoC router is nearly 73% of synchronous one. Power consumption of them are evaluated according to the obtained transition counting. Based on the comparison the power consumption of buffer and switch components are almost same due to their similar structure. However, the power consumption of routing unit component in asynchronous design is lower than synchronous one.

Blinded data mining is a branch of data mining technique which is focused on protecting user privacy. To mine sensitive data such as medical information, it is desirable to protect privacy and there is not worry about revealing personalized data. In this paper a new approach for blinded data mining is suggested. It is based on ontology and k-anonymity generalization method. Our method generalizes a private table by considering table fields’ ontology, so that each tuple will become k-anonymous and less specific to not reveal sensitive information. This method is implemented using protégé and java for evaluation.

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogobased traffic simulator to serve as the agents’ world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

In this paper we propose an artificial immune system in which learning automata are used to adaptively determine the values of its parameters. Learning automata are used for altering the shape of receptor portion of antibodies to better complementarily match the confronted antigen. In order to show the effectiveness of the proposed artificial immune computer experiments have been conducted. The result of experimentations confirms the effectiveness of the proposed model.

A collection of web pages which are about a common topic and are created by individuals or any kind of associations that have a common interest on that specific topic is called a web community. Since at present, the size of the web is over 3 billion pages and it is still growing very fast, identification of web communities has become an increasingly hard task. In this paper, a method based on asynchronous cellular learning automata (ACLA) for identification of web communities is proposed. In the proposed method first an asynchronous cellular learning automaton is used to determine the related pages and their relevance degree (the relationship structure of web pages). For determination of relationship structure of web pages information about hyperlinks and the users’ behaviour in visiting the web pages are used. Then, an algorithm similar to the HITS algorithm is applied on the obtained structure to identify the web communities. One of the advantages of the proposed method is that the web community obtained using this method is not dependent on a specific web graph structure. To evaluate the proposed approach, it is implemented and the results are compared with the results obtained for two existing methods, HITS and a complete bipartite graph based method. Experimental results show the superiority of the proposed method.

A user of Brain Computer Interface (BCI) system must be able to control external computer devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. There are problems associated with classification of different BCI tasks. In this paper we propose the use of chaotic indices of the BCI. We use largest Lyapunov exponent, mutual information, correlation dimension and minimum embedding dimension as the features for the classification of EEG signals which have been released by BCI Competition IV. A multi-layer Perceptron classifier and a KMSVM( support vector machine classifier based on kmeans clustering) is used for classification process, which lead us to an accuracy of 95.5%, for discrimination between two motor imagery tasks.

A hybrid feature and decision level information fusion architecture is proposed for human emotion recognition from facial expression and speech prosody. An active buffer stores the most recent information extracted from face and speech. This buffer allows fusion of asynchronous information through keeping track of individual modality updates. The contents of the buffer will be fused at feature level; if their respective update times are close to each other. Based on the classifiers’ reliability, a decision level fusion block combines results of the unimodal speech and face based systems and the feature level fusion based classifier. Experimental results on a database of 12 people show that the proposed fusion architecture performs better than unimodal classification, pure feature level fusion and decision level fusion.

Performance of wavelet thresholding methods for speech enhancement is dependent on estimating an exact threshold value in the wavelet sub-bands. In this paper, we propose a new method for more exact estimating the threshold value. We proposed to determine the threshold value based on the symmetric Kullback-Leibler divergence between the probability distributions of noisy speech and noise wavelet coefficients. In the next step, we improved this value using segmental SNR. We used some of TIMIT utterances to assess the performance of the proposed threshold. The algorithm is evaluated using the PESQ score and the SNR improvement. In average, we obtain 2db SNR improvement and a PESQ score increase up to 0.7 in comparison to the conventional wavelet thresholding approaches.

In this paper, we propose a new method for mobile robot path planning in dynamic environment when the trajectories of obstacles are unknown. Our algorithm first utilizes a global approach called clearance based probabilistic roadmap method to find a suitable path and then locally apply evolutionary algorithm to keep the structure of the path when obstacles collide with the path. As a result, the path will act like an elastic band. To reach real time applicability, a light fitness function is proposed compare to other genetic algorithms to reduce the computation time needed for calculating and repairing path. Simulation results show that our method not only can keep the original structure of path, but also repair operation is done quickly even in the scenes with many obstacles.

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The Markov property of images is addressed in post-processing to join separate neighbor skin detected regions. Comparison of the proposed method with other state-of-the-art methods shows a lower false positive rate with a little decrease in true positive rate.

Job-shop scheduling problem is demonstrated as one of the NP-complete problems. To solve this problem, we propose two types of hybrid shuffled frog leaping algorithm. Hybrid algorithms are generated by combining the shuffled frog leaping and a local search method. Also a new local search method by combining two other simple local searches is proposed. The obtained results demonstrate that our proposed hybrid algorithms have a better performance than their nonhybrid competitors. Also a comparison among proposed hybrid shuffled frog leaping and hybrid genetic algorithms demonstrate that the hybrid shuffled frog leaping algorithms can be generated a better schedule than their genetic algorithm competitors. A caparison of the best obtained results with the results reported in the considered literature shows that our proposed algorithms have a comparable performance.

Accurate pupil segmentation is the first and most important step for an iris recognition system. Current methods are based on fitting a model such as circle or ellipse to find and detect pupil, while these methods don’t have sufficient accuracy and are sensitive to the specular spot reflection. In this paper, we utilize an optimized color mapping to increase the accuracy of pupil segmentation, regardless of pupil model and its shape (circular or elliptic), while removing the effects of specular spot reflection. The optimum color mapping can be established by an iterative minimization algorithm similar to Levenberg- Marquardt (LM) method. By applying this method, a new image is provided with a clear pupil region that can be easily segmented. Also a robust preprocessing step is presented in this paper that sharpens and clears pupil region. We obtain 98% accuracy in pupil boundary detection by applying this method on UBIRIS dataset. Also, the proposed method works well on any model of eye image even where the eye is not perpendicular to the camera.

We address, in this paper, the problem of estimating the input sequence of a known, non-minimum phase, FIR system, when a large number of its roots are located near or on the unit circle. This issue cannot be solved by conventional methods known to date. Recently, algorithms based on spectral factorization are considered as possible solutions of inversing nonminimum phase systems but, these techniques cannot prohibit the instability of the systems whose roots are located on the unit circle. We propose an alternative method based on adaptive filtering resulted from a new point of view of the deconvolution problem that avoids inversing the system. The LMS adaptive filter is used to meet our objective while faster implementation than optimization-based techniques, be it gradient based or genetic, is achieved. Moreover, the technique is validated by experimental results, in simulated cases, which are mainly focused on large sequence of signals in noisy conditions.

In this work, we present SIMUIINK bit-true modeling of the conjugate structure-algebraic CELP (CS-ACELP) speech coder which has been chosen as the core layer of speech coder standard ITU-T G.729.1. The optimum bit numbers of the computational blocks are defined as the minimum word-widths that maintain the quality of the output with minimum chip area and power. Such optimum bitwidth of the coefficients and the internal computations are extracted. As a result, a golden model of the codec which best suits as a reference for its hardware implementation is developed. The power and area improvements are estimated in two blocks of CSACELP speech coder.